Data From: Multimodal OCT with deep learning reveals in vivo healing dynamics in hydrogel-treated wounds

Monitoring hydrogel-guided wound healing with high spatial resolution without invasive procedures remains a key clinical challenge. We developed an integrated platform combining optical coherence tomography (OCT) with artificial intelligence (AI) to quantitatively monitor biomaterial-modulated tissue regeneration. Using AI-assisted 3D segmentation and enhanced speckle variance OCT, we tracked longitudinal changes in granulation tissue, dermal restoration, and vascular remodeling in wounds treated with hydrogels of tunable stiffness. This multimodal OCT-AI platform enabled quantitative characterization of stiffness-dependent differences in wound healing progress and trajectories. Stiff hydrogels promoted earlier vascular remodeling and accelerated healing, whereas soft hydrogels exhibited delayed phase transitions and slower overall healing. These OCT-derived measurements were well-aligned with histological and immunofluorescence analyses, confirming earlier inflammation resolution in stiff hydrogel-treated wounds and validating the accuracy and sensitivity of this non-invasive approach. This multimodal OCT-AI platform shows strong potential for preclinical treatment evaluation and clinical wound monitoring.